Self-contained navigation system and method

ABSTRACT

The present invention relates to an auto locating system for finding the location of a viewpoint comprising: (a) at least one sensor for acquiring samples of the skyline view of said viewpoint; (b) at least one memory device, for storing Digital Terrain Map (DTM) related data; (c) at least one processor for processing said samples of said skyline view from said at least one sensor and for comparing the data derived from said samples with the data calculated from said DTM data for finding the location of said viewpoint; and (d) at least one output for outputting the location of said viewpoint.

TECHNICAL FIELD

The present invention relates to navigation systems and methods. Moreparticularly, the present invention relates to self-contained electronicnavigation systems.

BACKGROUND

As of today, electronic navigation systems, such as GPS, rely onexternal radiating inputs, such as satellites. Unfortunately, theseexternal radiating inputs are not always available, and therefore arenot always reliable.

Other forms of navigation methods are also available today, such asinertial systems, e.g. “dead reckoning”, which are a form of navigationthat determines position of a vehicle by advancing a previous positionto a new one on the basis of assumed moved distance and direction. Acompass is used to indicate direction, and distance may be determinedindirectly by measurement of speed and time, or measured directly aswell. Nevertheless, the actual direction of travel and the actualdistance may differ from the assumed direction and distance.

US 2012/0290199 discloses a method for self-orientation in a threedimensional environment by using a mapped data of the environment and anumber of linear measurements. The mapped data of the environment hasdigitized data related to a number of points in the three dimensionalenvironment. As disclosed in the publication, by performing a pluralityof linear measurements to another feature of the environment it ispossible to self-orient the location. The measurements comprisedetermining the distance between the object, and the relative azimuthbetween them, and the relative height between them.

It would therefore be desired to propose a system void of thesedeficiencies.

SUMMARY

It is an object of the present invention to provide a self-containednavigation method and system that does not rely on external radiatinginputs or the knowledge of an initial known point.

It is another object of the present invention to provide a method andsystem for auto positioning that does not require more than one sensor.

It is still another object of the present invention to provide a newelectronic navigation system which is size, cost and energy efficient.

Other objects and advantages of the invention will become apparent asthe description proceeds.

The present invention relates to an auto locating system for finding thelocation of a viewpoint comprising: (a) at least one sensor foracquiring samples of the skyline view of said viewpoint; (b) at leastone memory device, for storing Digital Terrain Map (DTM) related data;(c) at least one processor for processing said samples of said skylineview from said at least one sensor and for comparing the data derivedfrom said samples with the data calculated from said DTM data forfinding the location of said viewpoint; and (d) at least one output foroutputting the location of said viewpoint.

In one embodiment the sensor is an inclinometer.

In one embodiment the sensor is an imaging sensor.

In one embodiment the imaging sensor is an infrared sensor.

In one embodiment the imaging sensor is a Terahertz sensor.

In one embodiment the system has image intensification capabilities.

In one embodiment the DTM is the DTM of a certain area.

In one embodiment the DTM is the DTM of the whole world.

Preferably, the system is also used for North finding.

Preferably, the system is also used for calculating the azimuth of theviewpoint.

Preferably, the system is implemented in a single device.

In one embodiment the system is used for zeroing an inertial navigationsystem.

In one embodiment the system is used for aiding a GPS system.

The present invention also relates to a method for auto locating aviewpoint comprising the steps of: (a) providing at least one maximalelevation data set of a DTM point; (b) providing at least one sensorcapable of acquiring samples of the skyline view of said viewpoint; (c)receiving said samples related to said skyline view of said viewpointacquired from said sensor; (d) deriving the maximal elevation data setfrom said data samples of said skyline view; (e) comparing said maximalelevation data set from said samples with said at least one maximalelevation data set of at least one of said DTM's points; (f) finding thelocation of said viewpoint based on said comparison; and (g) outputtingdata related to said location of said viewpoint.

In one embodiment the method is used for auto locating a viewpoint whichis higher than ground level.

In one embodiment the maximal elevation data set, comprises maximalelevation angles related to the gravity.

In one embodiment the maximal elevation data set, comprises maximalelevation angles that are relative to other samples of the skyline view.

Preferably, the maximal elevation data set of a DTM point is found by:(a) calculating the visible areas of said point; (b) finding thefarthest visible points of said visible areas of said point whichconstitute the skyline; (c) calculating the elevation angles, for atleast some of said found furthest visible points; and (d) storing atleast some of said elevation angles as a maximal elevation data set.

In one embodiment the comparison is done using correlation techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, and specific references to their details, areherein used, by way of example only, to illustratively describe some ofthe embodiments of the invention.

In the drawings:

FIG. 1 is a visual 3D model interpretation of a Digital Terrain Map(DTM).

FIG. 2 is a schematic diagram of a process for calculating the maximalelevation data set for points on a DTM, according to an embodiment ofthe invention.

FIG. 3A depicts an example of a visual model interpretation of a topview of a DTM, according to an embodiment of the invention.

FIG. 3B depicts the maximal elevation points on the example model ofFIG. 3A, according to an embodiment of the invention.

FIG. 4 is a schematic diagram of an auto locating process using aninclinometer sensor, according to an embodiment of the invention.

FIG. 5 is a schematic diagram of an auto locating process using animaging sensor, according to an embodiment of the invention.

FIG. 6A depicts an example image of a part of a skyline from a cameraview, according to an embodiment of the invention.

FIG. 6B depicts an example of a skyline derived from the example imageof FIG. 6A, according to an embodiment of the invention.

FIG. 6C depicts an example of a visual model interpretation of a topview of a DTM, according to an embodiment of the invention.

FIG. 6D depicts an example of the maximal elevation points on the visualmodel interpretation of FIG. 5C, according to an embodiment of theinvention.

DETAILED DESCRIPTION

FIG. 1 is a visual 3D model interpretation of a Digital Terrain Map(DTM). The term “DTM”, is intended to include any Digital Terrain Map,Data Terrain Map, Digital Elevation Map, Data Digital Surface Model orany other digital description of the shape of a land, such as a landcover or the city surface. A DTM may comprise a data map of terrainelevations, at regularly spaced intervals. In one embodiment a DTMhaving a resolution of 50 m is used. In other embodiments, DTMs havingother resolutions may be used, such as a DTM having resolution of 1 kmor 1 meter or 1 cm. From the data of the DTM it may be possible tocalculate, for each point on the DTM, its specific visible area andhence its farthest visible points on the map, referred to hereinafter asthe skyline of the point. The skyline of a point can be used tocalculate a series of maximal elevation angles of the point. In oneembodiment, a maximal elevation angle of a point is the angle that canbe measured from the physical location of the point to one of the pointsof its physical skyline, i.e. the visible horizon, in respect to gravityor any other preset reference. For the sake of brevity, the physicallocation of a point is referred to hereinafter as the “viewpoint”, andthe physical skyline of the viewpoint is referred to hereinafter as the“skyline view”. The series of the maximal elevation angles of a singlepoint is referred to hereinafter as a “maximal elevation data set”. Inone embodiment, a series of the maximal elevation data set may comprise3 or more maximal elevation angles. In one embodiment the series of themaximal elevation angles comprises 10 maximal elevation angles. Inanother embodiment the series of the maximal elevation angles comprisesapproximately 20 maximal elevation angles. Therefore, if in a certainlocation, a user measures the angles, from his viewpoint to his skylineview; his location may be calculated and found. In other words, bycomparing between: the maximal elevation data set of a skyline view,measured by a user in a viewpoint, to the calculated maximal elevationdata sets of the skyline of the points on the DTM it is possible to findthe location of the viewpoint of the user. This is based on the factthat the skyline surrounding a certain point on the map typicallydiffers from the skyline surrounding a different point on the map, andthe maximal elevation data set measured in a viewpoint differs from theseries of maximal elevation data set measured from a differentviewpoint. Furthermore, by using the maximal elevation data setscalculated for the points on the DTM, it is possible to find not onlythe specific viewpoint location but also its direction, i.e. itsazimuth, otherwise known as “north finding”.

In one embodiment, a DTM of a certain area is used where the user candetermine his specific location on map based on calculations made on thepoints of that DTM. In this embodiment the used DTM may be the DTM ofthe country or area of the user or selected in any other way. In some ofthe cases the user may be notified that he is not present at any pointon the used DTM. In this case, if desired, another DTM may be added andused. In another embodiment a DTM of the whole world may be used.

FIG. 2 is a schematic diagram of a process for calculating the maximalelevation data set for points on a DTM, according to an embodiment ofthe invention. In step 10 a first point from the DTM is selected. Instep 11, the visible areas, of the selected point, are calculated andfound. The visible areas are the points where an imaginary line from theselected point to these points on the DTM is unobstructed. The visiblearea of the selected point may be calculated using any processingtechniques such as using the “VIEWSHED” function of MATLAB on the dataof the DTM points. In step 12 the farthest visible points in the visibleareas of the selected point are found which constitute the skyline ofthe selected point. A farthest visible point is found by selecting acertain imaginary line from the point and finding all the points whichoverlap or are relatively close to this imaginary line, then, selectingout of these close or adjoined points the furthest point from theselected point. The points selected as close, may be found using thefunction “ROUND” in MATLAB, or using any other known method. In oneembodiment, the closeness of the points may be a function of theresolution of the DTM. In other words finding the furthest visible pointof a direction may be done by checking the Euclidean distance betweenthe visible points on the DTM in a certain direction to the selectedpoint, and determining, for a certain direction, which specific visiblepoint on the DTM has the maximal distance from the selected point. Forexample: If the Euclidian distance between two points, (x1,y1) and(x2,y2) on DTM is the distance((x1,y1),(x2,y2))=squareroot{(x1-y1)̂2+(x2-y2)̂2}, and for the max distance in a certaindirection: Max{distance(x1, y1),(x[i], y[i])}=Max [square root {(x1-y1)̂2+(x[i]-y[i])̂2}], i=1, 2, 3 . . . n, which are all points on DTM in acertain direction from (x1, y1). After finding the farthest visiblepoint of a certain direction another imaginary line may be selected.This new imaginary line may have any offset angle such as 0.1 degreesfrom the first imaginary line. In one embodiment the offset angle may beselected according to the distance of the furthest visible points, sincethe span between the sequential farthest visible points depends upon thedistance from the selected point and the span between sequential angles.Thus more imaginary lines may be selected in this manner where each hasan offset angle from its predecessor imaginary line. In one embodimentthe process continues until a full circumference of 360 degrees isachieved. For example if a fixed offset angle of 0.1 degrees isselected, then, 3600 imaginary lines may be selected and processed.Other embodiments may have different fixed offset angles or unfixedoffset angles. In step 13, the elevation angles, for each of thefurthest visible points, is calculated by calculating, for each of thefarthest visible points, its differentiate height, in relation to thepoint, and its distance from the point. Each pair, the differentiateheight and the distance, of a farthest visible point, constitute thetangent of the elevation angle of this farthest visible point. Thus theterm “elevation angle” is meant to include the vertical degree, i.e.inclination, between a point and another point. In step 14, each of theelevation angles, found in step 13, for each of the furthest visiblepoints is set and stored in relations to the selected point. If themaximal elevation angles data set has been calculated for all thedesired points, then, in step 15, the process continues to step 17.However, if the maximal elevation angles data set has not beencalculated for all the desired points on the DTM, then, in step 15, theprocess continues to step 16 where another point on the DTM is selected,and steps 11 to 15 are repeated. When the process reaches step 17, aftercalculating the maximal elevation angles for all the desired points ofthe DTM, they are stored as vectors or in any other way. In oneembodiment the desired points are all the points of the DTM, where theprocess continues calculating the maximal elevation angles for all thepoints of the DTM. In another embodiment the desired points are some ofthe points of the DTM, such as: selecting only a fourth of the points,of the DTM, having regularly spaced intervals between them, or any otherselection of points. In yet another embodiment, the process maycalculate the maximal elevation angles for some of the points of the DTMas a first step and then, based on results or any other constraint, theprocess may continue only for the points within certain proximity.

FIG. 3A depicts an example of a visual model interpretation of a topview of a DTM, according to an embodiment of the invention. In thisexample, the white triangle depicts the viewpoint, and the black squaresdepict the areas visible from the viewpoint on the DTM map.

FIG. 3B depicts the maximal elevation points on the example model ofFIG. 3A, according to an embodiment of the invention. In this example,the white triangle is the viewpoint as well, however, the black squaresare the farthest visible points derived from the visible areas of theviewpoint.

FIG. 4 is a schematic diagram of an auto locating process using aninclinometer sensor, according to an embodiment of the invention. Theterm “inclinometer” is meant to include any know instrument formeasuring angles of elevation and depression such as a MEMS sensor,accelerometer, electrolytic, mercury, gas bubble liquid or pendulum. Forexample, a dual-axis digital inclinometer and accelerometer made byAnalog Devices Model: ADIS16209 may be used. In this embodiment, theinclinometer may be used as an elevation sensor in order to find theelevation angles between the viewpoint and the points of its skylineview. For example, the user can gather the data needed by tracking theskyline view while sampling and saving the measurements from theinclinometer. In step 21, the user can acquire at least 1 sample, i.e.reading from the inclinometer, of the skyline view from his viewpoint.In one embodiment 10 different samples may be acquired, in otherembodiments, 20 different samples or any other number of differentsamples may be acquired. In one embodiment, the sampling of the skylineis done consistently, for example: the user may sample the skyline viewaround the viewpoint in clockwise direction, or for example: sample theskyline of all range peaks and/or ridge, or any other samplingtechnique. In one embodiment the samples are taken in relativelyuniformly offset angles, such as every 18 degrees. In step 22, themaximal elevation data set is derived from the inclinometer's samples.In one embodiment the inclinometer outputs the elevation angles it isaimed at, when it is aimed at the skyline view, and these angles may beaggregated together to form the maximal elevation data set of theviewpoint. In step 23 the maximal elevation data set derived from theinclinometer is compared with the maximal elevation data set calculatedfor the DTM points, as described in relations to FIG. 2. In step 24 thelocation of the viewpoint may be found based on the comparisons fromstep 23. In addition the North may be found as well as a relation to theposition, i.e. the azimuth, of the user. This may be done by comparingthe last sample from the inclinometer, or by calculating the cyclicconvolution order of the samples.

For the sake of brevity an example is set forth for carrying out anembodiment of the invention:

Set below are an example of the maximal elevation points, which togethercreate a maximal elevation data set. As can be seen in the table below,the derived data is for example point (X=32, Y=21) on an example DTMmap:

Delta height Projection Viewpoint is Height between the DistanceCalculated X = 32 y = 21 in DTM at point and in meters elevation angleheight = 370.17 X Y (x, y) point skyline point (pixel = 50 m) from DTMPoint on skyline 20 31 396.85 26.6800 70.7107 20.6721 Point on skyline17 30 421.75 51.5800 223.6068 12.9894 Point on skyline 16 30 424.4354.0600 269.2582 11.3526 Point on skyline 15 31 400.4 30.2300 304.13815.6763 Point on skyline 15 32 370.88 0.7100 300.0000 0.1356 Point onskyline 14 35 355.88 −14.2900 380.7887 −2.1492 Point on skyline 18 35394.42 24.2500 212.1320 6.5215 Point on skyline 19 35 407.33 37.1600180.2776 11.6471 Point on skyline 20 35 422.51 52.3400 158.1139 18.3159Point on skyline 21 35 430.57 60.4000 150.0000 21.9330

Thus, each point of the DTM has its own maximal elevation data set.After producing all the maximal elevation data sets for each of thedesired points of the DTM, it can be compared to the maximal elevationsdata set calculated from the samples taken by the user:

Continuing the example above, the angles measured by the user using aninclinometer are:

20.67 12.98 11.35 5.67 0.13 −2.14 6.525 11.64 18.31 21.93

Now it is possible to compare the maximal elevation data sets from thepoints of the DTM to the maximal elevation data set the user hasmeasured at his viewpoint. As shown above, the calculated elevationangles from the DTM at point (X=32, Y=21) on DTM matches the usermeasurement at the unknown location. Therefore, the user measured hismaximal elevation data set from viewpoint located at (X=32, Y=21) on themap.

The comparison between the maximum elevation data set measured by theuser and between the maximum elevation data sets calculated from the DTMdata can be done in few methods; one of them is correlation between themaximum elevation data set measured by the user and the maximumelevation data sets that were produced for the points of the DTM.

Correlation results with Original point of view (x = 32 y = 21) maximalelevation data set: maximal elevation data set at (x = 32 y = 21) 0.9812maximal elevation data set at (x = 32 y = 20) 0.7289 maximal elevationdata set at (x = 31 y = 21) 0.5464 maximal elevation data set at (x = 32y = 14) 0.6734

In the table above we can see the correlation results for correlationbetween the maximal elevation data set measured by the user and thedifferent elevation data sets calculated from the DTM. As shown in thetable above, there is a good correlation score when comparing themaximal elevation data sets of a certain point on map to the usermeasured maximal elevation data set at the same certain point.

In one embodiment, the term auto locating may include the ability todetermine the location of a viewpoint on map.

FIG. 5 is a schematic diagram of an auto locating process using animaging sensor, according to an embodiment of the invention. Forexample, an imaging sensor may be a FLIR First Mate II MS-224b NTSC240×180 Thermal Night Vision Camera. In this embodiment, an imagingsensor may be used in order to find the angles between the viewpoint andpoints of its skyline view, and thus may be used for auto positioning.For example, a user at a viewpoint can aim the imaging sensor to theskyline view and acquire images of the skyline view from differentdirections. In one embodiment 4 images may be taken for example: using 4sensors where each one is aimed, for example, to a different sectorhaving 90 degrees angles in between, or in another embodiment, using 1sensor with a beam splitter for a total of 4 directions. In otherembodiments, a single sensor may be used, for example: using a sensorand rotating it in an offset angle, where the degree of the rotationoffset angle can be as the fixed aperture of the lens, or as the userrotates the imaging sensor to the end of the last captured sector, or byusing an automatic method of image processing. In one embodiment thenumber of sectors depends on the lens aperture. In step 31, the user canacquire frames of the land and skyline border from his viewpoint usingthe imaging sensor. In step 32, the border between the landscape and thesky, i.e. the skyline, is derived, using image processing techniquessuch as edge detection—where a “mathematical filter” can detect wherethe image brightness changes sharply, this sharp change occurs betweenthe sky and the land. In step 33 the maximal elevation data set can bebuilt according to the sensor aperture, and the calculated elevationangles to the skyline. For example, if the degrees represented by eachline on an image sensor=(total vertical aperture in degrees) divided by(number of lines in image sensor), and if, for example, the imagingsensor has 1000 lines from the top to the bottom of the acquired frame,there is a total of 1000 pixels that comprise the vertical aperture.Furthermore, if the aperture is 10 degrees, then, each pixel in thevertical axes represents 10/1000=0.01 degrees. After calculating thepixels to degree conversion, it is possible by means of imageprocessing, for example: edge detection, to determine the absoluteelevations in the acquired frame. If required the same technique may beapplied for the horizontal, i.e. the azimuthal, aperture. In step 34 themaximal elevation data set built from the imaging sensor is comparedwith the maximal elevation data from the DTM points, as described inrelations to step 17 of FIG. 2. In step 35 the location of the viewpointmay be found based on the comparisons from step 34. In addition theNorth may be found as well as a relation to the position, i.e. theazimuth, of the user. This may be done by comparing the last image fromthe imaging sensor, or by any other means.

In other embodiments different imaging sensors may be used such asinfrared sensors and thermal sensors. Other imaging techniques may alsobe used, for example: Image intensification technologies which enhancethe moonlight and starlight, Active illumination technologies which useadditional source of light typically in the IR band, and thermal imagingwhich detect changes in temperature of objects. In one embodiment a FLIRFirst Mate II MS-224b NTSC 240×180 Thermal Night Vision Camera may beused for acquiring the images for the auto locating process. In anotherembodiment, the imaging sensor may be a Terahertz sensor.

FIG. 6A depicts an example image of a part of a skyline from a cameraview, according to an embodiment of the invention. In this embodiment, auser which is located at an unknown point on map which is within theborders of an available DTM can use a camera or any other imaging systemto find his position. Since the imaging sensor has a known aperture, bycapturing the skyline, surrounding the user, it is possible to derivethe maximum elevation angles of the viewpoint. Thus in order to get themaximal elevation angles, from the sample pixels, it is possible to useknown methods of image processing. For example, since the color of skyis significantly different from the color of land it is possible toderive the skyline pixels using a known image process called edgedetection.

FIG. 6B depicts an example of a skyline derived from the example imageof FIG. 6A, according to an embodiment of the invention. Since theskyline, of the viewpoint, can be derived in this example, it ispossible to derive the maximal elevation angles to the skyline as well.For example, if the camera's sensor is leveled, and opens in a 6 degreeaperture, and if the specific point of the derived skyline is exactly inthe middle of the captured frame, the elevation angle of this specificpoint is 0. In another example, when a specific point in the derivedskyline is in row 120, counting from top of captured frame, out of 480rows of the sampled frame, the elevation angle from viewpoint is 1.5degrees, as each quarter of frame is 1.5 degrees. In this way theelevation angle of each pixel can be found and a maximal elevation dataset can be built.

FIG. 6C depicts an example of a visual model interpretation of a topview of a DTM, according to an embodiment of the invention. In thisexample, the white triangle depicts the viewpoint, and the black squaresdepict the areas visible from the viewpoint on the DTM map.

FIG. 6D depicts an example of the maximal elevation points on the visualmodel interpretation of FIG. 5C, according to an embodiment of theinvention. In this example, the white triangle is the viewpoint as well,however, the black squares are the maximal distance points visible fromthe viewpoint.

Since the tangent of the “maximal elevation angle” equals to the deltabetween the height of a certain viewpoint to a point of its skyline viewdivided by the length of the projection to the viewpoint, thecalculation can be done, for example, by moving in a pixel by pixel stepfrom the specific viewpoint toward a certain direction and calculatingby means of geometry for each step the delta height between theviewpoint to the defined skyline view, and on the other hand of theequation the known position of the pixel, which is the skyline derivedfrom DTM for the specific pixel. Thus the maximal elevation data set istransformed to a data set of points positioned on the DTM and related tothe viewpoint position. The beginning elevation specific verticalangular point may be unknown at first, however, it may be definedarbitrarily. Then, while sampling the skyline view, the delta betweenthe elevations can be derived and the maximal elevation data set of theuser viewpoint can be acquired. In this embodiment, the maximalelevation data set may comprise maximal elevation angles which arerelative to other points of the skyline view, or to other samples, or toany other reference. Nevertheless, defining an arbitrary first elevationdoes not change the outline of the maximal elevation data set vector.Thus the user's derived maximal elevation data set can be compared tothe DTM's maximal elevation data sets. The comparison may proceed forexample using methods of image or shape correlation, such as thefunction “corr2” in MATLAB. Thus it is possible to find the mostcomparable shape between the maximal elevation data set of the user andthe maximal elevation data sets vectors of the DTM. Another option, forexample, is to compare the maximal elevation points' data sets of theDTM, each time, to a newly defined arbitrary angle. For example, eachtime ascending in one degree and comparing, where the best comparisonachieved will be defined as the position of the user.

In one example, by comparing between: the maximal elevation data set ofthe skyline view measured by a user in an unknown location to thecalculated maximal elevation data set of the skyline of each point onDTM, and finding the most similar angles, the location, in terms of anx, y point on the DTM, of the user can be found.

In one embodiment, the described method may be used for finding thelocation of a viewpoint that is higher than the surface of the land. Forexample, the described method may be used for navigating in ahelicopter, or when the viewpoint is on bridge or on building, or anyother location elevated from the surface. If the user is higher than theground level in a certain viewpoint, and if his height from the groundlevel is known or can be fairly estimated, it is possible to calculatethe position of the user using the described method by taking intoaccount of the calculations the height of the user. However, in thiscase, the described calculations of the maximal elevation data set ofthe DTM slightly differ, as they are calculated in relations to theheight of the user. This is mainly due to the skyline view which differsfrom the skyline of a user on ground level. Thus the building of themaximal elevation data set may be done by adding the height of the userand calculating for each desired point its maximal elevation data setfrom the raised user point. Then the user's maximal elevation data setcan be compared to the new calculated maximal elevation data sets forthe heightened points of DTM. The points on DTM, which the maximalelevation datasets are measured for, are the DTM points which the userheight has been added to.

The auto locating system for finding the location of a viewpoint on amap may have at least one sensor for acquiring samples of the skylineview of the viewpoint. In one embodiment, the sensor may be aninclinometer such as Model:ADIS16209, dual-axis digital inclinometer andaccelerometer made by Analog Devices. In another embodiment the sensormay be an imaging sensor such as the FLIR First Mate II MS-224b NTSC240×180 Thermal Night Vision Camera. The system may also have at leastone memory device such as a non-volatile memory SD Card Sandisk 16 GBand/or a volatile memory such as the SDRAM: 64 MBIT 133 MHZ produced byMicron technology Inc. The memory device may be used for storing the DTMdata and/or storing the data samples from the sensor. The system mayalso have a processor such as the FPGA Cyclone, EP4CE115F23C8N byALTERA. The processor may be used for processing the data samples fromthe sensor and comparing it with the data calculated from the DTM datafor finding the location of a viewpoint on the DTM. The system may alsohave at least one digital output for outputting the location of saidviewpoint such as an LCD module, 16*1 characters, LCM—s01601DTR producedby Lumex opto components Inc. The output may be used for outputting thefound location of the user. In one embodiment the digital output is ascreen which can display a map of the area to the user with his foundlocation displayed on the map.

In one embodiment the calculation of the maximal elevation angles fromthe DTM may be done by the processor of the system which processes thereadings from the sensor. In this embodiment the system may be capableto self-locate the user once the DTM map is loaded and once the usertakes samples of his skyline view. In another embodiment instead ofloading a DTM for calculating and extracting the maximal elevation datasets, a set of maximal elevation data sets is loaded into the systemthus preserving the power of the system and eliminating the need tocalculate maximal elevation data sets. Both of these last embodimentsare especially helpful when desired to design a self-contained devicefor auto locating a user.

In one embodiment the calculations of the maximal elevation data setsand the processing of the sensor samples and the comparison between thetwo may be made by a remote server. In this embodiment the device usedby the user can be designed for the purpose of acquiring samples of theskyline view. Once the samples of the skyline view are acquired they maybe sent, via any known way of communication wired or wirelessly, to aremote server or processor. The remote processor may receive the sampleof the skyline view and derive from them the maximal elevation data set.The maximal elevation data sets may then be compared to maximalelevation data sets already stored by the server for finding acomparison. Once a comparison is found, the found location may be sentback to the device for notifying the user. In one of the cases thedevice may derive the maximal elevation data set from the samples of theskyline view and send this maximal elevation data set to the remoteserver for comparison. Thus when the memory and/or processor arerestricted, the user may send his measured maximal elevation dataset,partial or whole, to the remote server and all calculations can be doneby the remote server, after which, the result of calculation, which isthe user's position, can be sent to the user. Other configurations maybe used as well for practicing the described method.

In one embodiment the method and system described above may be used forzeroing an inertial system. As known in the art, an inertial system mayrequire zeroing as these forms of navigations determines position byadvancing a previous position to a new one on the basis of assumed moveddistance and direction. Since the actual distance and direction maydiffer from the assumed direction and distance these systems may requirezeroing. Thus the described system and method may be used in combinationwith some of the inertial systems for zeroing these inertial systems andtuning them for better accuracy of navigation.

In another embodiment the method and system described above may be usedfor aiding the calculations of a GPS system. By using the describedmethod for finding the initial location, this initial location may beused for aiding the GPS calculations.

In one embodiment the described method and system may be used for urbannavigation, where the used DTM may be a Data Digital Surface Model ofthe city or area, and where the user may be located on the street,bridge, building, or any other urban structure.

While some embodiments of the invention have been described by way ofexample, the invention can be carried into practice with manymodifications, variations, adaptations, and with the use of numerousequivalents or alternative solutions that are within the scope ofpersons skilled in the art, without departing from the invention orexceeding the scope of claims.

1. An auto locating system for finding the location of a viewpointcomprising: a. at least one sensor for acquiring samples of the skylineview of said viewpoint; b. at least one memory device, for storing DTMrelated data; c. at least one processor for processing said samples ofsaid skyline view from said at least one sensor and for comparing thedata derived from said samples with the data calculated from said DTMdata for finding the location of said viewpoint; and d. at least oneoutput for outputting the location of said viewpoint.
 2. The system ofclaim 1, where the sensor is an inclinometer.
 3. The system of claim 1,where the sensor is an imaging sensor.
 4. The system of claim 3, wherethe imaging sensor is an infrared sensor.
 5. The system of claim 3,where the imaging sensor is a Terahertz sensor.
 6. The system of claim3, where the system has image intensification capabilities.
 7. Thesystem of claim 1, where the DTM is the DTM of a certain area.
 8. Thesystem of claim 1, where the DTM is the DTM of the whole world.
 9. Thesystem of claim 1, where the system is also used for North finding. 10.The system of claim 1, where the system is also used for calculating theazimuth of the viewpoint.
 11. The system of claim 1, where the system isimplemented in a single device.
 12. The system of claim 1, where thesystem is used for zeroing an inertial navigation system.
 13. The systemof claim 1, where the system is used for aiding a GPS system.
 14. Amethod for auto locating a viewpoint comprising the steps of: a.providing at least one maximal elevation data set of a DTM point; b.providing at least one sensor capable of acquiring samples of theskyline view of said viewpoint; c. receiving said samples related tosaid skyline view of said viewpoint acquired from said sensor; d.deriving the maximal elevation data set from said data samples of saidskyline view; e. comparing said maximal elevation data set from saidsamples with said at least one maximal elevation data set of at leastone of said DTM's points; f. finding the location of said viewpointbased on said comparison; and g. outputting data related to saidlocation of said viewpoint.
 15. The method of claim 14, where the methodis used for auto locating a viewpoint which is higher than ground level.16. The method of claim 14, where the maximal elevation data set,comprises maximal elevation angles related to the gravity.
 17. Themethod of claim 14, where the maximal elevation data set, comprisesmaximal elevation angles that are relative to other samples of theskyline view.
 18. The method of claim 14, where the maximal elevationdata set of a DTM point is found by: a. calculating the visible areas ofsaid point; b. finding the farthest visible points of said visible areasof said point which constitute the skyline; c. calculating the elevationangles, for at least some of said found furthest visible points; and d.storing at least some of said elevation angles as a maximal elevationdata set.
 19. The method of claim 14, where the comparison is done usingcorrelation techniques.